2-[18F]fluoro-2-deoxy-d-glucose positron emission tomography (2-[18F]FDG-PET) has an emerging supportive role in dementia diagnostic as distinctive metabolic patterns are specific for Alzheimer's disease (AD), dementia with Lewy bodies (DLB) and frontotemporal dementia (FTD). Previous studies have demonstrated that a data-driven decision model based on the disease state index (DSI) classifier supports clinicians in the differential diagnosis of dementia by using different combinations of diagnostic tests and biomarkers. Until now, this model has not included 2-[18F]FDG-PET data. The objective of the study was to evaluate 2-[18F]FDG-PET biomarkers combined with commonly used diagnostic tests in the differential diagnosis of dementia using the DSI classifier. We included data from 259 subjects diagnosed with AD, DLB, FTD, vascular dementia (VaD), and subjective cognitive decline from two independent study cohorts. We also evaluated three 2-[18F]FDG-PET biomarkers (anterior vs. posterior index (API-PET), occipital vs. temporal index, and cingulate island sign) to improve the classification accuracy for both FTD and DLB. We found that the addition of 2-[18F]FDG-PET biomarkers to cognitive tests, CSF and MRI biomarkers considerably improved the classification accuracy for all pairwise comparisons of DLB (balanced accuracies: DLB vs. AD from 64% to 77%; DLB vs. FTD from 71% to 92%; and DLB vs. VaD from 71% to 84%). The two 2-[18F]FDG-PET biomarkers, API-PET and occipital vs. temporal index, improved the accuracy for FTD and DLB, especially as compared to AD. Moreover, different combinations of diagnostic tests were valuable to differentiate specific subtypes of dementia. In conclusion, this study demonstrated that the addition of 2-[18F]FDG-PET to commonly used diagnostic tests provided complementary information that may help clinicians in diagnosing patients, particularly for differentiating between patients with FTD, DLB, and AD.

中文翻译：

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使用数据驱动的决策模型评估2- [18F] FDG-PET在痴呆鉴别诊断中的作用。

2- [18F]氟-2-脱氧-d-葡萄糖正电子发射断层显像（2- [18F] FDG-PET）在痴呆症诊断中具有新兴的支持作用，因为独特的代谢模式特定于阿尔茨海默氏病（AD），痴呆路易体（DLB）和额颞叶性痴呆（FTD）。先前的研究表明，基于疾病状态指数（DSI）分类器的数据驱动决策模型可通过使用诊断测试和生物标记物的不同组合来支持临床医生进行痴呆症的鉴别诊断。到目前为止，该模型尚未包含2- [18F] FDG-PET数据。该研究的目的是使用DSI分类器评估2- [18F] FDG-PET生物标志物与常用诊断测试相结合对痴呆的鉴别诊断。我们纳入了259位被诊断患有AD，DLB，FTD，血管性痴呆（VaD），和两个独立研究队列的主观认知下降。我们还评估了三个2- [18F] FDG-PET生物标志物（前项对后项指数（API-PET），枕项对时态指数和扣带状岛征），以提高FTD和DLB的分类准确性。我们发现，将2- [18F] FDG-PET生物标记物添加到认知测试，CSF和MRI生物标记物中，大大提高了所有成对比较DLB的分类准确性（平衡精度：DLB与AD从64％增至77％； DLB与FTD的比率从71％增至92％； DLB与VaD的比率从71％增至84％）。两种2- [18F] FDG-PET生物标志物API-PET和枕位相对时间指数提高了FTD和DLB的准确性，尤其是与AD相比。此外，诊断测试的不同组合对于区分痴呆的特定亚型是有价值的。结论，